Embodiments relate to databases and in particular to efficient monitoring and analysis of large data volumes, for example to identify Governance, Risk, and Compliance (GRC) issues.
Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Current business environments are experiencing the generation of increasingly large volumes of business data. The growing amounts of business data that are available, can offer both opportunities and challenges to an enterprise.
Specifically, large volumes of business data can offer unparalleled visibility for garnering detailed insight into past and present business activity. Such insight can in turn provide the basis for predicting into future events, for example the accurate extrapolation of growth trajectories.
The availability of large volumes of business data can also give rise to potential challenges. In particular, an enterprise may need to recognize and handle certain types of information in a manner compliant with various existing domestic, international, and internal regulatory regime (e.g. relating to issues such as privacy and/or security).
Accordingly, the ability to efficiently and accurately analyze large volumes of data to identify compliance and other issues, is desirable. Even more desirable is for such analysis of large data volumes to take place in a manner accessible to ordinary business users.
The present disclosure addresses these and other issues with methods and apparatuses providing configurable rules for automatically monitoring large volumes of data in an in memory database.